Much has been documented in the literature on sentiment analysis and document summarisation. Much of this applies to long structured text in the form of documents and blog posts. With a shift in social media towards short commentary (see Facebook status updates and twitter tweets), the difference in comment structure may affect the accuracy of sentiment analysis techniques. From our VoiceYourView trial, we collected over 2000 individual short comments on the topic of library refurbishment, many of which are transcribed spoken comments. We have shown success in determining the theme of comments by looking for the first noun and using a semantic tag set to categorise this noun and hence the comment for short comments. Sentiment is a measure of how positive or negative a comment is, and the actionability metric is a measure of how actionable the comment is, i.e. how useful it is. This paper looks towards applying methods from the literature to our dataset with the aim of evaluating methods of automatic sentiment and actionability analysis for our VoiceyourView application data and has relevance to data from other applications, e.g. those from the social media. With many social media commentary applications moving to add speech platforms, VoiceYourView data may be representative of the type of free-form spoken text input to be expected in such platforms.